Decision Analysis (DA): Definition, Uses, and Examples

What Is Decision Analysis (DA)?

Decision analysis (DA) is a systematic, quantitative, and visual approach to addressing and evaluating the important choices that businesses sometimes face. Ronald A. Howard, a professor of Management Science and Engineering at Stanford University, is credited with originating the term in 1964. The idea is used by large and small corporations alike when making various types of decisions, including management, operations, marketing, capital investments, or strategic choices.

Understanding Decision Analysis (DA)

Decision analysis uses a variety of tools to evaluate all relevant information to aid in the decision-making process and incorporates aspects of psychology, management techniques, training, and economics. It is often used to assess decisions that are made in the context of multiple variables and that have many possible outcomes or objectives. The process can be used by individuals or groups attempting to make a decision related to risk management, capital investments, and strategic business decisions.

Key Takeaways

  • Decision analysis is a systematic, quantitative, and visual approach to making strategic business decisions.
  • Decision analysis uses a variety of tools and also incorporates aspects of psychology, management techniques, and economics.
  • Risk, capital investments, and strategic business decisions are areas where decision analysis can be applied.
  • Decision trees and influence diagrams are visual representations that help in the analysis process.
  • Critics argue that decision analysis can easily lead to analysis paralysis and, due to information overload, the inability to make any decisions at all.

A graphical representation of alternatives and possible solutions, as well as challenges and uncertainties, can be created on a decision tree or influence diagram. More sophisticated computer models have also been developed to aid in the decision-analysis process.

The goal behind such tools is to provide decision-makers with alternatives when attempting to achieve objectives for the business, while also outlining uncertainties involved and providing measures of how well objectives will be reached if final outcomes are achieved. Uncertainties are typically expressed as probabilities, while frictions between conflicting objectives are viewed in terms of trade-offs and utility functions. That is, objectives are viewed in terms of how much they are worth or, if achieved, their expected value to the organization.

Despite the helpful nature of decision analysis, critics suggest that a major drawback to the approach is "analysis paralysis," which is the overthinking of a situation to the point that no decision can be made. In addition, some researchers who study the methodologies used by decision-makers argue that this type of analysis is not often utilized.

Examples of Decision Analysis

If a real estate development company is deciding on whether or not to build a new shopping center in a location, they might examine several pieces of input to aid in their decision-making process. These might include traffic at the proposed location on various days of the week at different times, the popularity of similar shopping centers in the area, financial demographics, local competition, and preferred shopping habits of the area population. All of these items can be put into a decision-analysis program and different simulations are run that help the company make a decision about the shopping center.

As another example, a company has a patent for a new product that is expected to see rapid sales for two years before becoming obsolete. The company is confronted with a choice of whether to sell the patent now or build the product in-house. Each option has opportunities, risks, and trade-offs, which can be analyzed with a decision tree that considers the benefits of selling the patent verses making the product in-house. Within those two branches of the tree, another group of decision trees can be created to consider such things as the optimal selling price for the patent or the costs and benefits of producing the product in-house.

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  1. Stanford University. "Stanford Profiles: Ronald Howard." Accessed June 23, 2021.

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